Journal of Healthcare Engineering

Modeling and Optimization of Healthcare Systems

Publishing date
01 Mar 2023
Submission deadline
04 Nov 2022

1Jordan University of Science and Technology, Irbid, Jordan

2Sanming University, Sanming, China

3Michigan Technological University, Houghton, USA

This issue is now closed for submissions.

Modeling and Optimization of Healthcare Systems

This issue is now closed for submissions.


Healthcare organizations are cost-intensive environments, and they must be managed efficiently. The fundamental role of effective healthcare systems is to provide low-cost, timely, and relevant access to health services for all patients. Optimization can therefore be divided into minimizing the cost of services, maximizing patient satisfaction, minimizing waiting time, maximizing fairness policy, and ensuring optimal cost efficiency. One of the principal keys to increasing the productivity of existing healthcare facilities is the proper management of operating rooms (OR), emergency departments (ED), diagnostic services, and therapeutic pathways, which can be done by optimizing the whole process or chain of processes involved in the management and treatment of a patient. In particular, OR management is the science of how to run an operating room suite, focusing on maximizing operational efficiency at the facility to maximize the number of surgical cases that can be done on a given day while minimizing the required resources and related costs. Strategic operating room management deals with long-term decision-making, while ED management is increasingly studied to ensure appropriate access to healthcare services promptly, maximizing the efficiency of used facilities and human and material resources, decreasing delays, and enhancing satisfaction among patients and healthcare staff.

The healthcare industry is currently facing a multitude of challenges, including global population ageing, a rise in chronic conditions and diseases, resource constraints, increasing costs, and a growing demand for preventive healthcare services. Technological improvements can aid in providing cost-effective and easily accessible healthcare services. For example, artificial intelligence (AI) and the Internet of Things (IoT) can help accelerate the delivery of healthcare services by allowing physicians to spend minimal time on logistics and diagnostics and more time on treatment. The unprecedented growth and development of AI has transformed health system administration, healthcare data analytics, and patient diagnosis and treatment. Despite this growth, health systems have yet to maximize the potential of AI to improve load balancing and optimization of patient throughput. With the help of wearable devices, care providers can remotely track factors like sleep, heart rate, temperature, physical activity, and blood pressure, and these devices can also facilitate the provision of remote medical assistance in emergencies. In addition, extensive patient data can be collected in less time and with fewer resources, to the benefit of statistical studies supporting medical research.

The aim of this Special Issue is to report high-quality research on recent advances in the modeling and optimization of healthcare systems, specifically the state-of-the-art approaches, challenges, and opportunities for the design, development, deployment, and innovative use of convergence technologies to provide insight into healthcare service demands. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

  • AI-empowered innovative optimization techniques and testbeds for healthcare systems
  • Data processing, evaluation, and selection, and planning and design methods for modeling healthcare management applications
  • AI-empowered big data analytics and cognitive computing for health monitoring and decision-making
  • Advanced AI-IoT convergent services, systems, infrastructure, and techniques for optimizing and modeling healthcare
  • AI-supported IoT data analytics for optimizing healthcare services
  • Algorithms for the analysis and processing of biomedical data to support decision-making
  • Machine learning-based smart homecare for mobile-enabled fall detection of disabled or elderly people
  • Intelligent IoT-driven diagnosis and prognosis mechanisms for infectious diseases
  • IoT cloud-based predictive analysis for personalized healthcare
  • AI-supported decision-making for treatment and classification of healthcare on IoT-cloud platforms
  • Security, privacy, and trust in AI-IoT convergent effective healthcare systems
Journal of Healthcare Engineering
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Acceptance rate8%
Submission to final decision133 days
Acceptance to publication34 days
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